Probabilistic
Graphical Models

10-708,
Fall 2006

School of Computer Science, Carnegie-MellonUniversity

Lectures: Wednesdays and Fridays,
noon-1:30pm in Wean 5409
Recitations: Thursdays 5-6:30pm in
Wean 4615A
Special Recitations: At some points in the semester, we will hold
special, optional recitations on Mondays
5:30-7pm in Wean 4615A, we will announce these events in advance

Course Description

Many of the problems
in artificial
intelligence, statistics, computer systems, computer vision, natural
language
processing, and computational biology, among many other fields, can be
viewed
as the search for a coherent global conclusion from local
information.
The general graphical models framework provides an unified view for
this wide
range of problems, enabling efficient inference, decision-making and
learning
in problems with a very large number of attributes and huge datasets.
This
graduate-level course will provide you with a strong foundation for
both
applying graphical models to complex problems and for addressing core
research
topics in graphical models.

The class will cover
three
aspects: The core representation, including Bayesian and Markov
networks,
dynamic Bayesian networks, and relational models; probabilistic
inference
algorithms, both exact and approximate; and, learning methods for both
the
parameters and the structure of graphical models. Students entering the
class
should have a pre-existing working knowledge of probability,
statistics, and
algorithms, though the class has been designed to allow students with a
strong
numerate background to catch up and fully participate.

Students are required
to have successfully
completed 10701/15781, or an equivalent class.